{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from readRinexObs import rinexobs\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from matplotlib.dates import YearLocator, MonthLocator, DateFormatter\n",
    "from pandas import DataFrame, Panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mah22800.15o is a RINEX 2.11 file.\n",
      "15.89 seconds for _block2df\n",
      "7.79 seconds for panel assignments\n",
      "finished in 25.66 seconds\n"
     ]
    }
   ],
   "source": [
    "data = rinexobs(\"mah22800.15o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script filters the data from the rinex file to look at only the first tlim points, which in this case turns out to be from about 6-7 UTC. It then further filters the points to only include satellites with a minimum of minpoints L1, L2, C1, and C2 values that aren't Nan. Finally only points with TEC values between 0 and 200 (in TECu, I believe) are included in the final dataframe, interesting_data. TEC is calculated according to the method in Bill Rideout's program, tec.py. NOTE: in his program, he only plots short blocks of data, about 30 seconds in length according to him, at a time, where there are no large (>1 TECu) jumps in TEC. I included all the data from 6-7 UTC and it is pretty messy. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-97c27c167955>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtlim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m4000\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mminpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3000\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m svs_with_pts_in_range = [i for i in data.items if(np.sum(np.logical_and.reduce(\n",
      "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,14))\n",
    "tlim = 4000\n",
    "minpoints = 3000\n",
    "\n",
    "svs_with_pts_in_range = [i for i in data.items if(np.sum(np.logical_and.reduce(\n",
    "                (data[data.labels[:tlim],i,'L1','data']>0,\n",
    "                 data[data.labels[:tlim],i,'L2','data']>0,\n",
    "                 data[data.labels[:tlim],i,'C1','data']>0,\n",
    "                 data[data.labels[:tlim],i,'C2','data']>0)))>minpoints)]\n",
    "\n",
    "#Bill's Calculation:\n",
    "convertL1ToMeters = 3.0E8/(154.0*10.23E6)\n",
    "convertL2ToMeters = 3.0E8/(120.0*10.23E6)\n",
    "f2f1Factor = 1.545727\n",
    "convertMetersToTEC = 6.158\n",
    "\n",
    "interesting_data = DataFrame()\n",
    "\n",
    "for sv in svs_with_pts_in_range:\n",
    "    diffrange = data[data.labels[:tlim],sv,'C2','data']-data[data.labels[:tlim],sv,'C1','data']\n",
    "    diffrange = diffrange*convertMetersToTEC*f2f1Factor\n",
    "    phase = f2f1Factor*(convertL1ToMeters*data[data.labels[:tlim],sv,'L1','data']\n",
    "                        -convertL2ToMeters*data[data.labels[:tlim],sv,'L2','data'])\n",
    "\n",
    "    phase = phase*convertMetersToTEC\n",
    "    median = sorted(list(phase-diffrange))[int(len(diffrange)/2)]\n",
    "    TEC = phase-median\n",
    "    interesting_data[sv]=TEC[np.logical_and(TEC<200,TEC>0)]\n",
    "\n",
    "    print(\"SVN: {}, Pts above 200: {}, Pts below zero: {}, NaNs: {}\".format(sv,\n",
    "                                                                            np.sum(TEC>200),\n",
    "                                                                            np.sum(TEC<0),\n",
    "                                                                            np.sum(np.isnan(TEC))))\n",
    "    \n",
    "    \n",
    "fig = plt.figure(figsize=(16,16))\n",
    "ax1 = plt.subplot(211)\n",
    "fmt = DateFormatter('%H:%M:%S')\n",
    "ax1.xaxis.set_major_formatter(fmt)\n",
    "ax1.autoscale_view()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('TECu')\n",
    "plt.title(\"Rideout's TEC Method\")\n",
    "\n",
    "lines = plt.plot(interesting_data)\n",
    "plt.legend(lines,interesting_data.columns)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I explored the loss of lock indicator. If the 0 bit is set in the lli (loss of lock indicator), that means that the receiver thinks it lost lock on the previous measurement. I made a mask of all the points where lock was lost across all four relevant parameters (L1,L2,C1,P2 in this case), and plotted those as red x's on top of the TEC plot. Underneath is the SNR (S1 and S2). As you can see, in this case, the lli looks pretty good but the SNR doesn't appear to show anything. Underneath all that is the signal strength indicator which maps the signal strength a value from 0 to 9. In this case it doesn't appear to show anything either.\n",
    "\n",
    "### SVN = 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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JSkU+2FassCHqTz5ZeP3o88+3oVvOFQ7TKispF5PEL5S2ba3MoWVLOyhdtcpm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\nv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aKvgPPAlv+2YND6QWbvN9eEXDbv98UfLEbP1T0xYvYIxCbEYunwpcjulf2J/X3Z2v7p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RZJ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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6369072278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svn = 23\n",
    "tlim2 = 8000\n",
    "\n",
    "lli = data[data.labels[:tlim2],svn,['L1','L2','C1','P2'],'lli']\n",
    "lli[np.isnan(lli)]=0\n",
    "lli=lli.astype(int)\n",
    "llimask = np.logical_or.reduce(lli%2)\n",
    "\n",
    "\n",
    "fig2 = plt.figure(figsize=(15,15))\n",
    "ax1 = plt.subplot(311)\n",
    "ax1.xaxis.set_major_formatter(fmt)\n",
    "plt.ylabel('TECu')\n",
    "plt.title('TEC w/ Loss of Lock Indicated')\n",
    "plt.grid()\n",
    "\n",
    "diffrange = data[data.labels[:tlim2],svn,'P2','data']-data[data.labels[:tlim2],svn,'C1','data']\n",
    "diffrange = diffrange*convertMetersToTEC*f2f1Factor\n",
    "phase = f2f1Factor*(convertL1ToMeters*data[data.labels[:tlim2],svn,'L1','data']\n",
    "                    -convertL2ToMeters*data[data.labels[:tlim2],svn,'L2','data'])\n",
    "\n",
    "phase = phase*convertMetersToTEC\n",
    "median = sorted(list(phase-diffrange))[int(len(diffrange)/2)]\n",
    "TEC = phase-median\n",
    "\n",
    "plt.plot(TEC[TEC<200])\n",
    "plt.plot(TEC[llimask][TEC[llimask]<200],'rx')\n",
    "\n",
    "ax2 = plt.subplot(312, sharex=ax1)\n",
    "plt.plot(data[data.labels,svn,'S1','data'][:tlim2])\n",
    "plt.plot(data[data.labels,svn,'S2','data'][:tlim2])\n",
    "plt.grid()\n",
    "plt.legend(['S1','S2'])\n",
    "plt.title('Signal to Noise')\n",
    "plt.yticks(np.arange(0,60,10))\n",
    "\n",
    "ax3 = plt.subplot(313, sharex=ax1)\n",
    "[plt.plot(data[data.labels[:tlim2],svn,a,'ssi']) for a in ['L1','L2','C1','P2']]\n",
    "plt.legend(['L1','L2','C1','P2'],bbox_to_anchor=(1.1, 1.05))\n",
    "plt.yticks(np.arange(10))\n",
    "plt.grid()\n",
    "plt.title(\"Signal Strength Indicator\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 21 (items) x 10 (major_axis) x 3 (minor_axis)\n",
       "Items axis: 2 to 32\n",
       "Major_axis axis: C1 to S5\n",
       "Minor_axis axis: data to ssi"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "start = datetime(year=2015,month=10,day=7,hour=6,minute=0,second=49)\n",
    "end = datetime(year=2015,month=10,day=7,hour=7,minute=7,second=30)\n",
    "data[end]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
